4. Case Study:
Virtual Knowledge Studio
http://simshelf2.virtualknowledgestudio.nl/activities/biggrid-wikipedia-experiment
● How do categories in WikiPedia
evolve over time? (And how do
they relate to internal links?)
● 2.7 TB raw text, single file
● Java application, searches for
categories in Wiki markup,
like [[Category:NAME]]
● Executed on the Grid
BioAssist Programmers' Day, January 21, 2011
5. 1.1) Copy file from local 2.1) Stream file from Grid 3.1) Process all files in
Machine to Grid storage Storage to single machine parallel: N machines
2.2) Cut into pieces of 10 GB run the Java application,
2.3) Stream back to Grid fetch a 10GB file as
Storage input, processing it, and
putting the result back
BioAssist Programmers' Day, January 21, 2011
6. Status Quo:
Arrange your own (Data-)parallelism
● Cut the dataset up in “processable chunks”:
● Size of chunk depending on local space on processing node...
● … on the total processing capacity available …
● … on the smallest unit of work (“largest grade of parallelism”)...
● … on the substance (sometimes you don't want many output files, e.g.
when building a search index).
● Submit the amount of jobs you consider necessary:
● To a cluster close to your data (270x10GB over WAN is a bad idea)
● Amount might depend on cluster capacity, amount of chunks, smallest
unit of work, substance...
When dealing with large data, let's say 100GB+, this is
ERROR PRONE, TIME CONSUMING AND NOT FOR NEWBIES!
BioAssist Programmers' Day, January 21, 2011
10. Hadoop Distributed File System (HDFS)
● Very large DFS. Order of magnitude:
– 10k nodes
– millions of files
– PetaBytes of storage
● Assumes “commodity hardware”:
– redundancy through replication
– failure handling and recovery
● Optimized for batch processing:
– locations of data exposed to computation
– high aggregate bandwidth
BioAssist Programmers' Day, January 21, 2011
http://www.slideshare.net/jhammerb/hdfs-architecture
11. HDFS Continued...
● Single Namespace for the entire cluster
● Data coherency
– Write-once-read-many model
– Only appending is supported for existing files
● Files are broken up in chunks (“blocks”)
– Blocksizes ranging from 64 to 256 MB, depending on configuration
– Blocks are distributed over nodes (a single FILE, existing of N
blocks, is stored on M nodes)
– Blocks are replicated and replications are distributed
● Client accesses the blocks of a file at the nodes directly
– This creates high aggregate bandwidth!
BioAssist Programmers' Day, January 21, 2011
http://www.slideshare.net/jhammerb/hdfs-architecture
12. HDFS NameNode & DataNodes
NameNode DataNode
● Manages File System Namespace ● A “Block Server”
– Mapping filename to blocks – Stores data in local FS
– Mapping blocks to DataNode – Stores metadata of a block (e.g. hash)
● Cluster Configuration – Serve (meta)data to clients
● Replication Management ● Facilitates pipeline to other DN's
BioAssist Programmers' Day, January 21, 2011
13. http://hadoop.apache.org/common/docs/r0.20.0/hdfs_shell.html
Metadata operations
● Communicate with NN only
– ls (see above), lsr, df, du, chmod,
chown... etc.
R/W (block) operations
● Communicate with NN and DN's
– put, copyFromLocal, CopyToLocal,
tail... etc.
BioAssist Programmers' Day, January 21, 2011
14. HDFS
Application Programming Interface (API)
● Enables programmers to access any HDFS from their code
● Described at
http://hadoop.apache.org/common/docs/r0.20.0/api/index.html
● Written in (and thus available for) Java, but...
● Is also exposed through Apache Thrift, so can be accessed
from:
● C++, Python, PHP, Ruby, and others
● See http://wiki.apache.org/hadoop/HDFS-APIs
● Has a separate C-API (libhdfs)
So: you can enable your services to work with HDFS
BioAssist Programmers' Day, January 21, 2011
15. MapReduce
● Is a framework for distributed (parallel) processing
of large datasets
● Provides a programming model
● Lets users plug-in own code
● Uses a common pattern:
cat | grep | sort | unique > file
input | map | shuffle | reduce > output
● Is useful for large scale data analytics and
processing
BioAssist Programmers' Day, January 21, 2011
16. MapReduce Continued...
● Is great for processing large datasets!
– Send computation to data, so little data over lines
– Uses blocks stored in the DFS, so no splitting required
(this is a bit more sophisticated depending on your input)
● Handles parallelism for you
– One map per block, if possible
● Scales basically linearly
– time_on_cluster = time_on_single_core / total_cores
● Java, but streaming possible (plus others, see later)
BioAssist Programmers' Day, January 21, 2011
17. MapReduce
JobTracker & TaskTrackers
JobTracker TaskTracker
● Holds job metadata ● Requests work from JT
– Status of job – Fetch the code to execute from the DFS
– Status of Tasks running on TTs – Apply job-specific configuration
● Decides on scheduling ● Communicate with JT on tasks:
● Delegates creation of 'InputSplits' – Sending output, Killing tasks, Task updates, etc
BioAssist Programmers' Day, January 21, 2011
19. MapReduce
Application Programming Interface (API)
● Enables programmers to write MapReduce jobs
● More info on MR jobs:
http://www.slideshare.net/evertlammerts/infodoc-6107350
● Enables programmers to communicate with a
JobTracker
● Submitting jobs, getting statuses, cancelling jobs,
etc
● Described at
http://hadoop.apache.org/common/docs/r0.20.0/api/index.html
BioAssist Programmers' Day, January 21, 2011
20. Case Study:
Virtual Knowledge Studio
1) Load file into 2) Submit code
HDFS to MR
BioAssist Programmers' Day, January 21, 2011
21. What's more on Hadoop?
Lots!
● Apache Pig http://pig.apache.org
– Analyze datasets in a high level language, “Pig Latin”
– Simple! SQL like. Extremely fast experiments.
– N-stage jobs (MR chaining!)
● Apache Hive http://hive.apache.org
– Data Warehousing
– Hive QL
● Apache Hbase http://hbase.apache.org
– BigTable implementation (Google)
– In-memory operation
– Performance good enough for websites (Facebook built its Messaging Platform on top of it)
● Yahoo! Oozie http://yahoo.github.com/oozie/
– Hadoop workflow engine
● Apache [AVRO | Chukwa | Hama | Mahout] and so on
● 3rd Party:
– ElephantBird
– Cloudera's Distribution for Hadoop
– Hue
– Yahoo's Distribution for Hadoop
BioAssist Programmers' Day, January 21, 2011
22. Hadoop @ SARA
A prototype cluster
● Since December 2010
● 20 cores for MR (TT's)
● 110 TB gross for HDFS (DN) (55TB net)
● Hue web-interface for job submission & management
● SFTP interface to HDFS
● Pig 0.8
● Hive
● Available for scientists / scientific programmers until May / June 2011
Towards a production infrastructure?
● Depending on results
It's open for you all as well: ask me for an account!
BioAssist Programmers' Day, January 21, 2011
24. Hadoop for:
● Large-scale data storage and processing
● Fundamental difference: data locality!
● Small files? Don't, but... Hadoop Archives (HAR)
● Archival? Don't. Use tape storage. (We have lots!)
● Very fast analytics (Pig!)
● For data-parallel applications (not good at crossproducts – use
Huygens or Lisa!)
● Legacy applications possible through piping / streaming (Weird
dependencies? Use Cloud!)
We'll do another Hackathon on Hadoop. Interested? Send me a mail!
BioAssist Programmers' Day, January 21, 2011